Earnouts in mergers and acquisitions: A game-theoretic option pricing approach

This paper presents a valuation approach for merger and acquisition (M&A) deals employing contingent earnouts. It is argued that these transactions have option-like features, and the paper uses a game-theoretic option approach to model the value of such claims. More specifically, the paper examines the impact of uncertainty on the optimal timing of M&A using earnouts, and it also investigates the impact of uncertainty on the terms of the earnout. Optimal earnout and initial payment combinations are endogenously derived from the model, and testable hypotheses are developed. The theoretical contribution of this paper is a dynamic decision-making model of the invest-to-learn option generated upon investment in an acquisition. The paper also offers practical implications for the design of acquisitions employing earnouts.